https://blog.bytebytego.com/p/best-practices-for-building-ai-agents
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“These principles were aimed at making language-model applications robust enough for production.”
“How does the scope keep each agent narrow and supervised?”
“The human is very much still in the loop, and that’s a context problem.”
“But how do you build agents that work reliably in production?”
“How does the state hold memory in software while the model stays stateless?”
“That decision arrives as structured output.”
“A loop with weak stopping conditions can run far longer than intended, consuming tokens and money with each pass.”
“How does the control flow keep the loop and its stopping conditions in deterministic code?”
“Here’s what we will cover: How does context control what the model sees on every call?”
“Are there any best practices?”
“Each failure points to a specific practice, grouped into the four areas below, beginning with the inputs.”
“A discipline called context engineering has grown up around these habits, and it’s a big topic in itself.”
“While the token spend shows it, the throughput doesn’t.”
“The design requires less branching logic, since the model handles the decisions.”
“How much should a single agent try to accomplish?”
“Control Flow Owning the inputs matters only if the surrounding code decides when the model runs and when the loop ends.”
“When a task spans several distinct jobs, several narrow agents compose under deterministic orchestration.”
“This fact explains why production teams add so many guardrails.”
“Intercom’s customer service product shows the pattern at scale.”
“A loop runs longer than intended.”
“The context window, which is the bundle of text the model reads on a given call, serves as the entire memory of the system.”
“The specifics have kept evolving since, and teams at companies like Anthropic, Cognition, and Intercom have added lessons of their own.”